| Title: | Demultiplexing oligo-barcoded scRNA-seq data using regression mixture models |
|---|---|
| Description: | A package for demultiplexing single-cell sequencing experiments of pooled cells labeled with barcode oligonucleotides. The package implements methods to fit regression mixture models for a probabilistic classification of cells, including multiplet detection. Demultiplexing error rates can be estimated, and methods for quality control are provided. |
| Authors: | Hans-Ulrich Klein [aut, cre] |
| Maintainer: | Hans-Ulrich Klein <[email protected]> |
| License: | Artistic-2.0 |
| Version: | 1.7.0 |
| Built: | 2024-07-24 04:29:56 UTC |
| Source: | https://github.com/bioc/demuxmix |
Cerebral spinal fluid (CSF) cells and peripheral blood mononuclear cells (PBMCs) were pooled and prepared for single-cell sequencing using the 10x Chromium System. Due to the low numbers of cells obtained from CSF, only the PBMCs but not the CSF cels were stained using oligonucleotide-labeled antibodies (BioLegend TotalSeq-A0257). CSF cells and PBMCs in this dataset were obtained from two genetically diverse individuals so that genetic demultiplexing could be used to validate the HTO-based demultiplexing. Genetic demultiplexing was performed with freemuxlet, which is part of the popscle software package.
data(csf)data(csf)
A data frame with 2,590 rows and 4 variables:
Number of HTO counts observed
Number of genes detected in the cell
Genetic demultiplexing result
Posterior probability from genetic demultiplexing in logarithmic scale
Raw sequencing data was aligned and processed using Cell Ranger 6.0.1. All droplets that passed Cell Ranger's default filtering step were read in. Genes with at least one read were considered as detected. Since Cell Ranger's threshold to identify non-empty droplets is relatively lenient, some droplets have as few as 30-50 genes detected. For most analyses, it is recommended to remove droplets with less than about 200 detected genes before demultiplexing.
Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, contact: Hans-Ulrich Klein ([email protected])
data(csf) csf <- csf[csf$NumGenes >= 200, ] hto <- t(matrix(csf$HTO, dimnames = list(rownames(csf), "HTO"))) dmm <- demuxmix(hto, model = "naive") summary(dmm) certain <- exp(csf$freemuxlet.prob) >= 0.999 table(dmmClassify(dmm)$HTO[certain], csf$freemuxlet[certain])data(csf) csf <- csf[csf$NumGenes >= 200, ] hto <- t(matrix(csf$HTO, dimnames = list(rownames(csf), "HTO"))) dmm <- demuxmix(hto, model = "naive") summary(dmm) certain <- exp(csf$freemuxlet.prob) >= 0.999 table(dmmClassify(dmm)$HTO[certain], csf$freemuxlet[certain])
This method uses mixture models as probabilistic framework to assign droplets to hashtags and to identify multiplets based on counts obtained from a hashtag oligonucleotide (HTO) library. If the numbers of detected genes from the corresponding RNA library are passed as second argument, regression mixture models may be used, which often improves the classification accuracy by leveraging the relationship between HTO and RNA read counts.
demuxmix( hto, rna, pAcpt = 0.9^nrow(hto), model = "auto", alpha = 0.9, beta = 0.9, correctTails = TRUE, tol = 10^-5, maxIter = 100, k.hto = 1.5, k.rna = 1.5, clusterInit = list() )demuxmix( hto, rna, pAcpt = 0.9^nrow(hto), model = "auto", alpha = 0.9, beta = 0.9, correctTails = TRUE, tol = 10^-5, maxIter = 100, k.hto = 1.5, k.rna = 1.5, clusterInit = list() )
hto |
A matrix of HTO counts where each row corresponds to a hashtag and each column to a droplet. The matrix must have unique row names. |
rna |
An optional numeric vector with the number of genes detected in
the RNA library for each droplet. Same length as columns in |
pAcpt |
Acceptance probability that must be reached in order to
assign a droplet to a hashtag. Droplets with lower probabilities are
classified as "uncertain". This parameter can be changed after running
demuxmix by applying |
model |
A character specifying the type of mixture model to be used.
Either "naive", "regpos", "reg" or "auto". The last three options
require parameter |
alpha |
Threshold defining the left tail of the mixture distribution where droplets should not be classified as "positive". Threshold must be between 0 and 1. See details. |
beta |
Threshold for defining the right tail of the mixture distribution where droplets should not be classified as "negative". Threshold must be between 0 and 1. See details. |
correctTails |
If |
tol |
Convergence criterion for the EM algorithm used to fit the mixture
models. The algorithm stops when the relative increase of the log
likelihood is less than or equal to |
maxIter |
Maximum number of iterations for the EM algorithm and for the alternating iteration process fitting the NB regression models within each EM iteration. |
k.hto |
Factor to define outliers in the HTO counts. Among droplets
positive for the hashtag based on initial clustering, HTO counts
larger than the 0.75 quantile + |
k.rna |
Factor to define outliers in the numbers of detected genes.
Numbers of detected genes larger than the 0.75 quantile +
|
clusterInit |
Optional list of numeric vectors to manually specify the
droplet to component assignment used to initialize the EM algorithm. The
name of each list element must match a row name of |
The single cell dataset should undergo basic filtering to
remove low quality or empty droplets before calling this function,
but the HTO counts should not be transformed or pre-processed
otherwise. The number of detected genes passed via the optional argument
rna is typically defined as the number of genes in the RNA library
with at least one read.
The method fits a two-component negative binomial mixture model for each
hashtag. The type of mixture model used can be specified by model.
"naive" fits a standard mixture model. "reg" fits a regression mixture
model using the given number of detected genes (rna) as covariate
in the regression model. "regpos" uses a regression model only for the
positive but not for the negative component. If model is set to "auto",
all three models are fitted and the model with the lowest posterior
classification error probability summed over all droplets is selected.
Details are stored in the slot modelSelection of the returned
object. In most real HTO datasets, regression mixture models outperform
the naive mixture model.
The demuxmix method consists of 3 steps, which can be tuned
by the respective parameters. The default settings work well for a wide
range of datasets and usually do not need to be adapted unless any issues
arise during model fitting and quality control. An exception is the
acceptance probability pAcpt, which may be set to smaller or
larger value depending on the desired trade-off between number of
unclassified/discarded droplets and expected error rate. Steps 1 and 2
are executed for each HTO separately; step 3 classifies the droplets based
on the results from all HTOs. Therefore, parameters affecting steps 1 and 2
(incl. model) can be specified for each HTO using a vector with
one element per HTO. Shorter vectors will be extended.
Preprocessing (k.hto, k.rna). Droplets are clustered
into a negative and a positive group based on the HTO counts using
k-means. Droplets in the positive group with HTO counts larger than the
0.75 quantile + k.hto times the IQR of the HTO counts in the
positive group are marked as outliers. Outliers are still classified but
will not be used to fit the mixture model for this HTO in step 2. If
the parameter rna is given and the model is "reg" or
"regpos", all droplets (both groups) with number of detected genes larger
than the 0.75 quantile + k.rna times the IQR are marked as outliers, too,
since these cells could affect the fitting of the regression model
negatively. If more than 15% of the cells are marked as outliers,
a warning message is printed and larger values for k.hto
and k.rna might be preferable. If the model fit seems to be
affected by a few large values (very high variance of the positive
component), smaller values should be chosen. On rare occasions, k-means
clustering can result in inadequate clusters, and the derived
distributional parameters are invalid. Poor clustering can be observed
if (i) the HTO failed and the distribution is not bimodal or (ii) the
fraction of positive cells tagged by the HTO is very small. An error
message is displayed, and if (ii) is determined as the cause, an initial
manual assignment can be specified by clusterInit to bypass
the k-means clustering.
Model fitting (model, alpha, beta,
correctTails, tol, maxIter). An EM algorithm is
used to fit the mixture model to the HTO counts which were not marked as
outliers in step 1. maxIter defines the maximum number of
iterations of the EM algorithm, and, if model is "reg", "regpos"
or "auto", it also defines the maximum number of iterations to fit the
negative binomial regression models within each EM iteration. tol
defines the convergence criterion for the EM algorithm. The algorithm
stops if tol. After the mixture model has
been fitted, the posterior probability that the i-th droplet is positive
for the hashtag is calculated. Depending on the given
data, these probabilities can be inaccurate at the far tails of the
mixture distribution. Specifically, a positive component with large
variance can have a larger value close to zero than the negative
component, if the negative component is narrow and shifted to the right
due to background HTO reads. If correctTails is TRUE, the
following two rules are applied to avoid false classifications at the
far tails. First, if the i-th droplet is classified as positive based on
the posterior probability, but the probability to detected more than the
observed HTO counts in a negative droplet is
> alpha, then is set
to 0 (left tail). Second, if the i-th droplet is classified as negative,
but > beta, is set to
1 (right tail). For most datasets, these rules will not apply and it is
recommended not to change these values. If correctTails is
FALSE, posterior probabilities will not be altered, but potential
problems at the tails will still be logged in the slot
tailException of the returned object.
Classification (pAcpt). The posterior probabilities
obtained from the models fitted to each HTO separately are
used to calculate the most likely class for each cell. The following
classes are considered: one class for each HTO (singlets), one
class for each possible multiplet, and a negative class representing
droplets negative for all HTOs (i.e. empty droplets or droplets
containing only cell debris). Each droplet is assigned to the most
likely class unless the probability is smaller than pAcpt,
in which case the droplet is assigned to the class "uncertain".
Classification results can be accessed by running
dmmClassify on an object returned by demuxmix. The
acceptance probability can be changed after running demuxmix using
pAcpt<-.
demuxmix returns an object of class Demuxmix.
Classification results can be extracted with dmmClassify.
Various plot methods (see below) are available to assess the model fit.
dmmClassify to extract the classification results
and summary to summarize the results.
plotDmmHistogram, plotDmmScatter,
plotDmmPosteriorP, and dmmOverlap to assess
the model fit.
set.seed(2642) simdata <- dmmSimulateHto(class = rbind(c(rep(TRUE, 220), rep(FALSE, 200)), c(rep(FALSE, 200), rep(TRUE, 220)))) dmm <- demuxmix(simdata$hto, model = "naive") dmm table(dmmClassify(dmm)$HTO, simdata$groundTruth) dmmreg <- demuxmix(simdata$hto, rna = simdata$rna) dmm table(dmmClassify(dmmreg)$HTO, simdata$groundTruth) summary(dmmreg) pAcpt(dmmreg) <- 0.5 summary(dmmreg) dmmOverlap(dmmreg) plotDmmHistogram(dmmreg) plotDmmScatter(dmmreg, hto="HTO_1")set.seed(2642) simdata <- dmmSimulateHto(class = rbind(c(rep(TRUE, 220), rep(FALSE, 200)), c(rep(FALSE, 200), rep(TRUE, 220)))) dmm <- demuxmix(simdata$hto, model = "naive") dmm table(dmmClassify(dmm)$HTO, simdata$groundTruth) dmmreg <- demuxmix(simdata$hto, rna = simdata$rna) dmm table(dmmClassify(dmmreg)$HTO, simdata$groundTruth) summary(dmmreg) pAcpt(dmmreg) <- 0.5 summary(dmmreg) dmmOverlap(dmmreg) plotDmmHistogram(dmmreg) plotDmmScatter(dmmreg, hto="HTO_1")
Objects of this class store mixture models fitted to HTO data to demultiplex
oligonucleotide-labeled cells. One mixture model is stored for each hashtag
in the dataset. An object of this class is returned by
demuxmix. Users should not directly initialize this class.
There are various methods to extract or plot data from a Demuxmix
object. Please see the package's vignette for how to work with an object of
this class.
## S4 method for signature 'Demuxmix' show(object) ## S4 method for signature 'Demuxmix' pAcpt(object) ## S4 replacement method for signature 'Demuxmix,numeric' pAcpt(object) <- value ## S4 method for signature 'Demuxmix' summary(object, ...)## S4 method for signature 'Demuxmix' show(object) ## S4 method for signature 'Demuxmix' pAcpt(object) ## S4 replacement method for signature 'Demuxmix,numeric' pAcpt(object) <- value ## S4 method for signature 'Demuxmix' summary(object, ...)
object |
A |
value |
Value between 0 and 1 specifying the acceptance probability, i.e., the minimum posterior probability required to assign a droplet to a hashtag. |
... |
Additional arguments forwarded to summary (ignored). |
All matrices stored by Demuxmix have the same dimension and
the same row and column names as the original matrix hto passed to
demuxmix. The mixture models in slot models are
stored in an internal class format.
An object of class Demuxmix.
show(Demuxmix): Displays the object on the command line.
pAcpt(Demuxmix): Returns the acceptance probability pAcpt.
pAcpt(object = Demuxmix) <- value: Sets a new acceptance probability pAcpt.
summary(Demuxmix): Summarizes the classification results and
estimates error rates.
modelsA list of mixture models. One model per HTO.
outliersA logical matrix of size HTOs x droplets identifying outlier values excluded from model fitting.
clusterInitA numeric matrix of size HTOs x droplets with the class memberships used to initialize model fitting. A value of 1 corresponds to the negative component and a value of 2 to the positive component.
posteriorProbA numeric matrix of size HTO x droplets with the posterior probabilities that a droplet is positive for an HTO.
tailExceptionA logical matrix of size HTO x droplets identifying
posterior probabilities that would be adjusted based on the exception
rules defined when calling demuxmix to correct inaccuracies
at the extreme tails of the mixture distributions. See
demuxmix for details.
modelSelectionA data.frame with information about the model
selection process if parameter model was set to 'auto'. Empty
data.frame if model was specified manually.
parametersA list with the demuxmix parameters
used to generate the model represented by this class.
dmmClassify to obtain classification results.
plotDmmHistogram, plotDmmScatter,
plotDmmPosteriorP, and dmmOverlap to assess
the model fit.
set.seed(2642) simdata <- dmmSimulateHto(class=rbind(c(rep(TRUE, 220), rep(FALSE, 200)), c(rep(FALSE, 200), rep(TRUE, 220)))) dmm <- demuxmix(simdata$hto, rna=simdata$rna, pAcpt=0.9) pAcpt(dmm) dmm head(dmmClassify(dmm))set.seed(2642) simdata <- dmmSimulateHto(class=rbind(c(rep(TRUE, 220), rep(FALSE, 200)), c(rep(FALSE, 200), rep(TRUE, 220)))) dmm <- demuxmix(simdata$hto, rna=simdata$rna, pAcpt=0.9) pAcpt(dmm) dmm head(dmmClassify(dmm))
This method uses the posterior probabilities from the given demuxmix model to assign each droplet to the most likely class, either a single HTO, a combination of HTOs (multiplet) or the negative class (non-labeled cells, empty droplets, cell debris). If the assignment cannot be made with certainty above a defined threshold, the droplet is labeled as "uncertain".
dmmClassify(object)dmmClassify(object)
object |
An object of class |
A droplet is labeled as "uncertain" if the posterior probability of
the most likely class is smaller than the threshold pAcpt, which
is stored in the given Demuxmix object. The acceptance
probability pAcpt can be inspected and set to a different value
by applying the getter/setter method pAcpt to the
Demuxmix object before calling this method. The method
summary is useful to inspect classification results and to
estimate error rates for different values of pAcpt.
A data.frame with 3 columns and one row for each droplet
in the dataset. The first column gives the class (HTO) the droplet has been
assigned to. The second column contains the posterior probability. And the
third column specifies the type of the assigned class, i.e., "singlet",
"multiplet", "negative" or "uncertain".
set.seed(2642) simdata <- dmmSimulateHto(class = rbind(c(rep(TRUE, 220), rep(FALSE, 200)), c(rep(FALSE, 200), rep(TRUE, 220)))) dmm <- demuxmix(simdata$hto, rna = simdata$rna) head(dmmClassify(dmm)) table(dmmClassify(dmm)$HTO, simdata$groundTruth) pAcpt(dmm) <- 0.5 sum(dmmClassify(dmm)$HTO == "uncertain") pAcpt(dmm) <- 0.9999 sum(dmmClassify(dmm)$HTO == "uncertain")set.seed(2642) simdata <- dmmSimulateHto(class = rbind(c(rep(TRUE, 220), rep(FALSE, 200)), c(rep(FALSE, 200), rep(TRUE, 220)))) dmm <- demuxmix(simdata$hto, rna = simdata$rna) head(dmmClassify(dmm)) table(dmmClassify(dmm)$HTO, simdata$groundTruth) pAcpt(dmm) <- 0.5 sum(dmmClassify(dmm)$HTO == "uncertain") pAcpt(dmm) <- 0.9999 sum(dmmClassify(dmm)$HTO == "uncertain")
dmmOverlap sums over the probability mass intersected by the two
components of the given mixture model. The sum should be
close to 0 if the HTO labeling experiment was successful.
dmmOverlap(object, hto, tol = 0.001)dmmOverlap(object, hto, tol = 0.001)
object |
An object of class |
hto |
Optional vector specifying a subset of HTOs in |
tol |
The maximum acceptable error when calculating the area. |
The probability mass shared between the negative and positive component is an informative quality metric for the labeling efficiency of the HTO. Values under 0.03 can be considered as good, values larger than 0.1 are problematic.
The probability mass functions of the negative and positive component are not scaled by the estimated proportions of negative and positive droplets. Therefore, the result does not depend on the proportion of cells stained with the HTO and the returned value lies between 0 and 1.
The definition of the shared probability mass is not obvious for a regression mixture model since the distributions' means depend on the covariate, i.e., the number of detected genes in the RNA library. If a regression mixture model is given, this method calculates for each of the two components the weighted mean number of detected genes and uses these numbers to calculate the expectation value for the negative and positive component respectively.
A numeric vector with the shared probability mass for each HTO in
the given object.
set.seed(2642) simdata <- dmmSimulateHto(class = rbind(c(rep(TRUE, 220), rep(FALSE, 200)), c(rep(FALSE, 200), rep(TRUE, 220)))) dmm <- demuxmix(simdata$hto, model = "naive") dmmOverlap(dmm) dmmreg <- demuxmix(simdata$hto, rna = simdata$rna) dmmOverlap(dmmreg) dmmOverlap(dmmreg, hto = "HTO_1") dmmOverlap(dmmreg, hto = 2)set.seed(2642) simdata <- dmmSimulateHto(class = rbind(c(rep(TRUE, 220), rep(FALSE, 200)), c(rep(FALSE, 200), rep(TRUE, 220)))) dmm <- demuxmix(simdata$hto, model = "naive") dmmOverlap(dmm) dmmreg <- demuxmix(simdata$hto, rna = simdata$rna) dmmOverlap(dmmreg) dmmOverlap(dmmreg, hto = "HTO_1") dmmOverlap(dmmreg, hto = 2)
This method simulates HTO count data and corresponding numbers of detected RNA features using the negative binomial distribution. The purpose of this method is to provide simple example datasets for testing and documentation.
dmmSimulateHto( class, mu = 180, theta = 15, muAmbient = 30, thetaAmbient = 10, muRna = 3000, thetaRna = 30 )dmmSimulateHto( class, mu = 180, theta = 15, muAmbient = 30, thetaAmbient = 10, muRna = 3000, thetaRna = 30 )
class |
A |
mu |
Vector of expectation values of the HTO counts if a
droplet is positive for the HTO. Values are recycled if |
theta |
Vector of dispersion parameters of the HTO counts if a
droplet is positive for the HTO. Values are recycled if |
muAmbient |
Vector of expectation values of the HTO counts if a
droplet is negative for the HTO. Values are recycled if |
thetaAmbient |
Vector of dispersion parameters of the HTO counts if a
droplet is negative for the HTO. Values are recycled if |
muRna |
Single expectation value for the number of detected RNA features. |
thetaRna |
Single dispersion parameter for the number of detected RNA features. |
A vector of detected RNA features (same length as columns
in class) is simulated using rnbinom with
muRna and thetaRna as parameters. HTO counts of positive
droplets are then simulated using rnbinom with
mu/muRna as expectation value and theta
as dispersion. If a droplet is negative for the HTO,
muAmbient/muRna and thetaAmbient
are used respectively.
A list with three elements: hto is a matrix of the same
dimension as the given class matrix and contains the simulated HTO
counts. rna is a vector of simulated detected number of genes (same
length as hto has columns). groundTruth is a character
vector encoding the class labels given by class as character
strings for convenience.
set.seed(2642) class <- rbind(c(rep(TRUE, 220), rep(FALSE, 200)), c(rep(FALSE, 200), rep(TRUE, 220))) simdata <- dmmSimulateHto(class = class, mu = c(150, 300), theta = c(15, 20), muAmbient = c(30, 30), thetaAmbient = c(10, 10), muRna = 3000, thetaRna = 30) dim(simdata$hto) table(simdata$groundTruth) mean(simdata$rna) # muRna var(simdata$rna) # muRna + muRna^2/thetaRna mean(simdata$hto[1, class[1, ]]) # mu[1] mean(simdata$hto[1, !class[1, ]]) # muAmbient[1] var(simdata$hto[1, class[1, ]]) # > mu[1] + mu[1]^2/theta[1] cor(simdata$rna[class[1, ]], simdata$hto[1, class[1, ]])set.seed(2642) class <- rbind(c(rep(TRUE, 220), rep(FALSE, 200)), c(rep(FALSE, 200), rep(TRUE, 220))) simdata <- dmmSimulateHto(class = class, mu = c(150, 300), theta = c(15, 20), muAmbient = c(30, 30), thetaAmbient = c(10, 10), muRna = 3000, thetaRna = 30) dim(simdata$hto) table(simdata$groundTruth) mean(simdata$rna) # muRna var(simdata$rna) # muRna + muRna^2/thetaRna mean(simdata$hto[1, class[1, ]]) # mu[1] mean(simdata$hto[1, !class[1, ]]) # muAmbient[1] var(simdata$hto[1, class[1, ]]) # > mu[1] + mu[1]^2/theta[1] cor(simdata$rna[class[1, ]], simdata$hto[1, class[1, ]])
This methods plots the mixture probability mass function with the negative
and positive component on top of a histogram of the HTO counts used to fit
the mixture model. The mixture model must be generated by
demuxmix.
plotDmmHistogram(object, hto, quantile = 0.95, binwidth = 5)plotDmmHistogram(object, hto, quantile = 0.95, binwidth = 5)
object |
An object of class |
hto |
Optional vector specifying a subset of HTOs in |
quantile |
Quantile of the mixture distribution which is used as right limit of the plot's x axis. |
binwidth |
Width of the bins of the histogram. |
A histogram overlaid with the pmf is a standard tool to assess the fit of a the mixture model and trivial for a naive mixture model. However, if a regression mixture model is given, the expectation values of the components are different for each droplet depending on the covariates (here the number of genes detected in the droplet). This method calculates the weighted mean number of detected genes in droplets in the positive and negative component, and then uses these numbers to calculate expectation values for an average droplet of the positive and negative component. The HTO counts shown in the histogram are adjusted to account for different numbers of detected genes by replacing the original HTO counts with the expected counts given the mean number of detected genes plus the residuals from the regression model. In other words, the effect of the number of detected genes was regressed out before plotting the HTO counts in the histogram.
It may be useful to zoom into the plot to obtain a better view
of the fit. To restrict the plot to a certain range on the x or y axis,
the method coord_cartesian from the ggplot2
package should be used (see examples).
An object of class ggplot is returned, if only one HTO is
plotted. If several HTOs are plotted simultaneously, a grid of plots is
returned.
set.seed(2642) simdata <- dmmSimulateHto(class = rbind(c(rep(TRUE, 220), rep(FALSE, 200)), c(rep(FALSE, 200), rep(TRUE, 220)))) dmm <- demuxmix(simdata$hto, simdata$rna) plotDmmHistogram(dmm) p <- plotDmmHistogram(dmm, hto = 1) p + ggplot2::coord_cartesian(xlim = c(25, 100), ylim = c(0, 0.01))set.seed(2642) simdata <- dmmSimulateHto(class = rbind(c(rep(TRUE, 220), rep(FALSE, 200)), c(rep(FALSE, 200), rep(TRUE, 220)))) dmm <- demuxmix(simdata$hto, simdata$rna) plotDmmHistogram(dmm) p <- plotDmmHistogram(dmm, hto = 1) p + ggplot2::coord_cartesian(xlim = c(25, 100), ylim = c(0, 0.01))
This methods plots a histogram of posterior probabilities obtained
from the given mixture model. The posterior probabilities indicate whether
the droplet likely contains a cell labeled by the respective HTO.
The mixture model passed to this function must be generated by
demuxmix.
plotDmmPosteriorP(object, hto, bins = 50)plotDmmPosteriorP(object, hto, bins = 50)
object |
An object of class |
hto |
Optional vector specifying a subset of HTOs in |
bins |
The number of bins of the histogram. |
The histogram visualizes how well the positive droplets can be
separated from the negative droplets. Ideally, the histogram shows many
droplets with a posterior probability very close to 0 and many droplets
close to 1, but no or very few droplets with probabilities somewhere in
between. The histogram can be useful for guiding the selection of the
acceptance probability pAcpt.
An object of class ggplot is returned, if only one HTO is
plotted. If several HTOs are plotted simultaneously, a grid of plots is
returned.
set.seed(2642) simdata <- dmmSimulateHto(class = rbind(c(rep(TRUE, 220), rep(FALSE, 200)), c(rep(FALSE, 200), rep(TRUE, 220)))) dmm <- demuxmix(simdata$hto, model = "naive") plotDmmPosteriorP(dmm) dmmreg <- demuxmix(simdata$hto, rna = simdata$rna, model = "auto") plotDmmPosteriorP(dmmreg) plotDmmPosteriorP(dmmreg, hto = 1)set.seed(2642) simdata <- dmmSimulateHto(class = rbind(c(rep(TRUE, 220), rep(FALSE, 200)), c(rep(FALSE, 200), rep(TRUE, 220)))) dmm <- demuxmix(simdata$hto, model = "naive") plotDmmPosteriorP(dmm) dmmreg <- demuxmix(simdata$hto, rna = simdata$rna, model = "auto") plotDmmPosteriorP(dmmreg) plotDmmPosteriorP(dmmreg, hto = 1)
This methods plots the number of genes detected in a droplet versus the
number of sequenced HTOs. The posterior probability that the droplet is
positive for the HTO is indicated by a color gradient. Optionally, the
decision boundary with posterior probability 0.5 can be plotted. The mixture
model passed to this function must be a regression mixture model
generated by demuxmix.
plotDmmScatter( object, hto, log = TRUE, pointsize = 1.2, plotDecBoundary = TRUE, tol = 0.01 )plotDmmScatter( object, hto, log = TRUE, pointsize = 1.2, plotDecBoundary = TRUE, tol = 0.01 )
object |
An object of class |
hto |
Optional vector specifying a subset of HTOs in |
log |
Logical value indicating whether both HTO counts and number of detected genes should be log transformed. |
pointsize |
Numeric value specifying the size of the points. |
plotDecBoundary |
Logical value indicating whether the decision boundary should be added to the plot. |
tol |
Numeric value between 0 and 1 specifying the error tolerance of
the decision boundary, i.e., a point on the plotted line has a posterior
probability within 0.5 +- |
The scatterplot produced by this method is helpful to assess
the relation between the number of detected genes and the number
of HTO counts obtained for a droplet. A positive association is usually
visible for the positive cells (i.e., droplets with cells treated with the
oligo-labeled antibodies). The association is often weak/absent in the
droplets negative for the HTO.
This method can only be applied to regression mixture models and not
to naive mixture models. To see whether a Demuxmix object
contains regression mixture models, type show(object) to display
the type of model used for each HTO.
An object of class ggplot is returned, if only one HTO is
plotted. If several HTOs are plotted simultaneously, a grid of plots is
returned.
set.seed(2642) simdata <- dmmSimulateHto(class = rbind(c(rep(TRUE, 220), rep(FALSE, 200)), c(rep(FALSE, 200), rep(TRUE, 220)))) dmmreg <- demuxmix(simdata$hto, rna = simdata$rna, model = "reg") plotDmmScatter(dmmreg) plotDmmScatter(dmmreg, hto = 1, log = FALSE)set.seed(2642) simdata <- dmmSimulateHto(class = rbind(c(rep(TRUE, 220), rep(FALSE, 200)), c(rep(FALSE, 200), rep(TRUE, 220)))) dmmreg <- demuxmix(simdata$hto, rna = simdata$rna, model = "reg") plotDmmScatter(dmmreg) plotDmmScatter(dmmreg, hto = 1, log = FALSE)
This method takes the demultiplexing results from an HTO experiment
returned by demuxmix and returns a data.frame
summarizing the classification results and expected error rates.
summary(object, ...)summary(object, ...)
object |
An object of class |
... |
Additional parameters (ignored). |
Results are summarized for the individual HTOs, for all singlets combined, for all multiplets combined, and for the negative class. Relative frequencies are calculated after excluding the "uncertain" class. The estimated number of false positive droplets and the estimated FDR are based on several assumptions, one of which is the independence of the HTO counts from different hashtags. This assumption is unlikely for real data where all HTO counts are obtained from the same droplet. Usually, the positive correlation among HTOs causes an overestimation of multiplets and negative/empty droplets. Error rates are more accurate when regression mixture models are used since the number of detected genes explains some of the positive correlation between HTOs.
A data.frame with one row per class showing the number
of droplets in the class (NumObs), the relative frequency of the class
(RelFreq), the median probability with which a droplet was assigned to the
class (MedProb), the estimated number of droplets falsely assigned to the
class (ExpFPs), and the corresponding estimated false discovery rate (FDR).
set.seed(2642) simdata <- dmmSimulateHto(class = rbind(c(rep(TRUE, 220), rep(FALSE, 200)), c(rep(FALSE, 200), rep(TRUE, 220)))) dmm <- demuxmix(simdata$hto, rna = simdata$rna) summary(dmm) pAcpt(dmm) <- 0.05 summary(dmm)set.seed(2642) simdata <- dmmSimulateHto(class = rbind(c(rep(TRUE, 220), rep(FALSE, 200)), c(rep(FALSE, 200), rep(TRUE, 220)))) dmm <- demuxmix(simdata$hto, rna = simdata$rna) summary(dmm) pAcpt(dmm) <- 0.05 summary(dmm)